QUality Estimation from ScraTCH (QUETCH): Deep Learning for Word-level Translation Quality Estimation

This paper describes the system submit-ted by the University of Heidelberg to the Shared Task on Word-level Quality Esti-mation at the 2015 Workshop on Statis-tical Machine Translation. The submit-ted system combines a continuous space deep neural network, that learns a bilin-gual feature representation from scratch, with a linear combination of the manually defined baseline features provided by the task organizers. A combination of these orthogonal information sources shows sig-nificant improvements over the combined systems, and produces very competitiv